The present invention relates generally to weather forecasts, and more specifically, to weather and seasonal forecasts generated from an assembly of forecast models.
Because of the importance of the weather, forecasts are readily available via a wide variety of media, including the Internet, television, radio and print media. Images of the weather generated by satellite photographs and radar networks are familiar to almost everyone. Nevertheless, despite a long history of the study of the atmosphere and its phenomena, and enormous technological and scientific advances, local, regional and seasonal forecasts often are inaccurate.
Meteorology as an exact science is a relatively recent science. However, as an inexact science, meteorology has been around for a long time. It is widely agreed that the word xe2x80x98meteorologyxe2x80x99 was coined by the Greek philosopher Aristotle, who wrote a book entitled Meteorologica circa 350 BC. In this work Aristotle attempted to explain atmospheric phenomena, such as clouds, wind, precipitation, lightning, thunder, and climatic changes. Although much of the work disclosed in Meteorologica was erroneous, it was not until the 17th century that his ideas were scientifically disproved.
The origins of meteorology as a natural science occurred in the late 16th century. At this time it had become evident that the speculations of philosophers regarding meteorology were inadequate and that better scientific knowledge was essential to understand the atmosphere. Therefore, it was realized that instruments were necessary to measure properties on Earth""s atmosphere. As a result, instruments such as the hygrometer, thermometer, and barometer were developed. These instruments measured atmospheric data and helped identify changes in weather. However, the weather forecasting effectiveness of such instruments, combined with the understanding of atmospheric processes at the time, were minimal.
It was not until the twentieth century that more detailed scientific instruments and knowledge were cultivated to help predict daily and seasonal climate changes. For instance, in the 1940s measurements of upper level meteorological components, such as temperature, humidity, pressure, and wind speed and direction, became possible by placing instruments on balloons that were released into the atmosphere. Meteorological science took an additional step forward in the 1950""s with the development of computers. Computers enabled models to be developed utilizing equations that approximated the physical processes of the atmosphere. These physical relationships are currently used in weather forecasting in an attempt to predict the future behavior of the atmosphere. To construct weather models computers use data collected from sophisticated instruments, such as RADAR and meteorological satellites, which provide monitoring of world weather events. Data from these instruments have been instrumental in improving our knowledge of all weather systems, including fronts, thunderstorms, hurricanes, and other weather events.
Weather and seasonal forecasting and prediction is a sophisticated art that utilizes measurements taken continuously from geographic areas around the globe. These measurements include temperature, wind speed, height of pressure gradient, humidity, precipitation, and the like, collected from weather balloons, weather stations, satellites, aircraft, buoys, and similar measurement equipment and/or facilities. Using these and other advancements in remote-sensing technologies to collect data, computer models for forecasting weather conditions have been developed.
Because of the collection and analysis of a vast amount of data from around the world and the numerical simulation of meteorological and climatological processes, supercomputers and the latest advanced mathematical techniques are an integral part of the science of the atmosphere. Using such equipment, one broad area of meteorological research encompasses the observation, numerical modeling, and prediction of weather systems such as hurricanes and severe storms. Today, sophisticated numerical models used in operational and research centers throughout the globe routinely make short-term (1 to 7 days in advance) weather and seasonal (one to several seasons in advance) climate forecasts. Models and projections are developed by the National Weather Service (the governmental entity in the United States charged with disseminating weather data to the public) and other private forecasting firms. Nevertheless, despite the vast scientific and technological improvements and advances in meteorology, including the modeling of weather using data accumulated by weather-monitoring instruments, weather prediction and models are often inaccurate, vague, broad, and lack regional or local specificity.
Because of the impact of weather on our daily lives, it is an understatement that it would be beneficial if we could predict both the short term and long term weather. Therefore, what is needed is a forecasting method and system that permits more accurate weather forecasts based upon the vast collection of data provided by scientific weather instruments. What is also needed is a forecasting method and system that can more accurately forecast climatological or seasonal weather changes.
Systems, methods and computer program products of the present invention collect historical forecast information generated from a plurality of weather models (or forecast models), where each model forecasted at least one predicted weather component. For example, a weather model may include a forecasted temperature (predicted weather component) for a specific geographical location for a certain past date or time period. Systems, methods and computer program products of the present invention compare the historical forecast information, and more particularly, the predicted weather components, generated by the plurality of weather models to observed weather data to determine the historical performance of the weather models. According to one aspect of the invention, the historical performance is the accuracy with which a weather model predicts a particular weather component. Thereafter, each model is weighted based upon the historical performance of that model in predicting a weather component at a particular geographic location or a range of geographical locations. The weighted weather models are then combined to generate a multi-model superensemble. The multi-model superensemble utilizes the historical performance of every weather model in forecasting weather components to generate a weather forecast for a future period of time.
More specifically, according to the present invention, a multi-model superensemble is developed using a plurality of forecasts from a variety of weather and climate models. Along with observed (or benchmark) analysis fields, these forecasts are used to derive statistics on the past behavior of the models. These statistics, combined with model forecasts, enables the construction of a superensemble forecast. More specifically, given a set of past model forecasts, the present invention uses a multiple regression technique to regress the model forecasts against observed (analysis) fields. Least-squares minimization of the difference between the model and the analysis field is used to determine the weights of each model component at any geographic location and vertical level.
According to one embodiment of the present invention, there is disclosed a method for generating an accurate weather forecast model. The method includes the steps of collecting historical forecast information from a plurality of weather models, wherein the historical forecast information includes at least one predicted weather component, and wherein the historical forecast information corresponds to a past period of time. The method also includes accumulating observed weather data, wherein the observed weather data corresponds to a plurality of known weather values, wherein at least one known weather value of the plurality of known weather values corresponds to the at least one predicted weather component, and wherein the observed weather data corresponds to the past period of time. The method further includes comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model of the plurality of weather models, and generating a multi-model superensemble of the weather models, wherein the multi-model superensemble is based upon the historical performance of each weather model of the plurality of weather models.
According to one aspect of the invention, comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model includes comparing the at least one known weather value to at least one predicted weather component. According to another aspect of the invention, comparing the at least one known weather value to the at least one predicted weather component includes calculating at least one weight factor for the at least one predicted weather component. Furthermore, comparing the at least one known weather value to the at least one predicted weather component can include calculating at least one weight factor for the at least one predicted weather component by least squares minimization.
According to yet another aspect of the present invention, generating a multi-model superensemble of the weather models includes generating a multi-model superensemble based upon a combination of weather models weighted by their respective historical performances. Additionally, generating a multi-model superensemble of the weather models can include generating a multi-model superensemble based upon a summation of the at least one weight factor for the at least one predicted weather component of each of the plurality of weather models.
The method can further include collecting future forecast information from the plurality of weather models corresponding to a future period of time, and wherein generating a multi-model superensemble includes generating a multi-model superensemble based upon the historical performance of each weather model of the plurality of weather models and the future forecast information. Moreover, generating a multi-model superensemble can include weighting the future forecast information from the plurality of weather models based upon the historical performance of each weather model of the plurality of weather models.
According to another embodiment of the present invention, there is disclosed a method for generating accurate weather forecasts. The method includes collecting historical forecast information from a plurality of weather models, wherein the historical forecast information includes at least one predicted weather component, and wherein the historical forecast information corresponds to a past period of time. The method also includes accumulating observed weather data corresponding to a plurality of known weather values, wherein at least one known weather value of the plurality of known weather values corresponds to the at least one predicted weather component, and wherein the observed weather data corresponds to the period of time. The method further includes comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model of the plurality of weather models, and calculating at least one weight for each weather model, based upon the historical performance of each weather model in forecasting the at least one predicted weather component. Finally, the method includes combining the weights for each weather model with future forecast information from the plurality of weather models, wherein the future forecast information corresponds to a future period of time, to generate a multi-model superensemble forecast.
According to one aspect of the invention, generating a multi-model superensemble forecast includes combining the weather models, wherein each model is weighted based on its respective weight. According to another aspect of the invention, comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model includes comparing the at least one known weather value to the at least one predicted weather component. According to yet another aspect of the present invention, comparing the at least one known weather value to the at least one predicted weather component includes calculating at least one weight factor for the at least one predicted weather component. Additionally, in the method of the present invention, comparing the at least one known weather value to the at least one predicted weather component can include calculating at least one weight factor for the at least one predicted weather component by least squares minimization.
According to yet another embodiment of the invention, there is disclosed a method for generating accurate weather forecasts. The method includes accumulating historical forecast information from a plurality of weather models, where the historical forecast information is derived prior to the occurrence of weather forecasted by the plurality of weather models, and wherein the historical forecast information includes a plurality of predicted weather components related to expected weather conditions. The method also includes collecting observed weather data after the occurrence of the weather forecasted by the plurality of weather models, wherein the observed weather data includes known weather values corresponding to at least some of the plurality of predicted weather components, and weighting the historical performance of each weather model in predicting the plurality of predicted weather components by comparing the accumulated historical forecast information to the observed weather data. Further, the method includes generating a superensemble weather model based upon a combination of each weighted weather model.
According to one aspect of the invention, weighting the historical performance of each weather model in predicting the plurality of predicted weather components includes weighting the historical performance of each weather model by a least squares minimization calculation between each weather model and the observed weather data. According to another aspect of the invention, generating a superensemble weather model based upon a combination of each weighted weather model includes combining each weighted weather model to develop a forecast for future weather conditions.
According to yet another embodiment of the present invention, there is disclosed a system for generating an accurate weather forecasting model. The system includes a plurality of weather models, wherein the weather models include historical forecasts for past weather conditions and prospective forecasts for future weather conditions, observed weather data corresponding to the past weather conditions, and a superensemble generator. The superensemble generator is in communication with the plurality of weather models and observed weather data, for producing a superensemble forecast, and the superensemble generator determines the historical performance of the plurality of weather models based on a comparison of the historical forecasts for past weather conditions to the observed weather data. Additionally, the superensemble forecast is based at least in part upon the historical performance of the plurality of weather models and the prospective forecasts for future weather conditions.
According to one aspect of the invention, the historical forecasts include at least one predicted weather component, wherein the observed weather data corresponds to a plurality of known weather values, and wherein at least one known weather value of the plurality of known weather values corresponds to the at least one predicted weather component. According to another aspect of the invention, the observed weather data consists of data selected from the group consisting of precipitation, temperature, wind speed and direction, height, pressure, atmospheric moisture content, and tropical cyclone positions and intensities. According to yet another aspect of the invention, the superensemble generator is in communication with the plurality of weather models via the Internet, a wide area network, or a local area network. Finally, according to another aspect of the invention, the superensemble generator includes a processor, and a superensemble module in communication with said processor, wherein the superensemble module and processor operate to compare the historical forecasts to the observed weather data to determine the historical performance of the plurality of weather model.
According to yet another embodiment of the present invention, there is disclosed a computer program product for generating an accurate weather forecast model, comprising a computer readable storage medium having computer-readable program code means embodied in said medium. The computer-readable program code means include computer-readable program code means for collecting historical forecast information from a plurality of weather models, wherein the historical forecast information includes at least one predicted weather component, and wherein the historical forecast information corresponds to a past period of time, and computer-readable program code means for accumulating observed weather data, wherein the observed weather data corresponds to a plurality of known weather values, wherein at least one known weather value of the plurality of known weather values corresponds to the at least one predicted weather component, and wherein the observed weather data corresponds to the past period of time. The computer readable program code means also include computer readable program code means for comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model of the plurality of weather models, and computer-readable program code means for generating a multi-model superensemble of the weather models, wherein the multi-model superensemble is based upon the historical performance of each weather model of the plurality of weather models.
According to one aspect of the invention, the computer-readable program code means for comparing the historical forecast information to the observed weather data to determine the historical performance of each weather model comprises computer-readable program code means for comparing the at least one known weather value to the at least one predicted weather component. According to another aspect of the invention, the computer-readable program code means for comparing the at least one known weather value to the at least one predicted weather component comprises computer-readable program code means for calculating at least one weight factor for the at least one predicted weather component. According to yet another aspect of the invention, the computer-readable program code means for comparing the at least one known weather value to the at least one predicted weather component comprises computer-readable program code means for calculating at least one weight factor for the at least one predicted weather component by least squares minimization. Furthermore, the computer-readable program code means for generating a multi-model superensemble of the weather models can comprise computer-readable program code means for generating a multi-model superensemble based upon a combination of weather models weighted by their respective historical performances.
According to yet another aspect of the invention, the computer-readable program code means for generating a multi-model superensemble of the weather models comprises computer-readable program code means for generating a multi-model superensemble based upon a summation of the at least one weight factor for the at least one predicted weather component of each of the plurality of weather models. The computer readable program code means can additionally include computer-readable program code means for collecting future forecast information from the plurality of weather models corresponding to a future period of time, and wherein the computer-readable program code means for generating a multi-model superensemble comprises computer-readable program code means for generating a multi-model superensemble based upon the historical performance of each weather model of the plurality of weather models and the future forecast information. Finally, the computer-readable program code means for generating a multi-model superensemble can include computer-readable program code means for weighting the future forecast information from the plurality of weather models based upon the historical performance of each weather model of the plurality of weather models.